MAF1731 - Microstructure and Trading Systems

B.Eng Financial Engineering

Giusti Gutiérrez Ana Paula

Rivera Sahagun Juan Pablo

Paris Merin Douglas Fabián

October 2021 | Repository: Link

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Lab 3

Metrics and Behavioral Finance

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Abstract

Include here a description of the whole work, use no more than 400 words, you can use **Bold** and *Italic* highlights, also you can inclue [links](www.iteso.mx). Usually is a good strategy to generate this text at the very end of the process, since you have to include all aspects of the work, from a brief introduction, to problem definitions and methods used, to finally the results found.

1. Introduction

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This is an introduction, normally, it is done at the very end, when all the other content in the notebook is completed but just before the abstract (which is short version of the introduction) ...

2. Install/Load Packages and Depedencies

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2.1 Python Packages

In order to run this notebook, it is necessary to have installed and/or have the requirements.txt file with the following:

• pandas>=1.1.1
• numpy>=1.19.1
• jupyter>=1.0.0
• yfinance~=0.1.63
• MetaTrader5~=5.0.34
• plotly>=5.3.1
• Some others that can be used.

2.2 Files Dependencies

The following are the file dependencies that are needed to run this notebook:

• files/History5400732.xlsx : Student Operations History 1
• files/History5401437.xlsx : Student Operations History 2
• files/df_hist_5401675.csv : Student Operations History 3
• files/instruments_pips.csv : Data base with pips of a list of instrument

2.3 Install and Import Packages

%%capture

# Install all the pip packages in the requirements.txt
import sys
!{sys.executable} -m pip install -r requirements.txt

import functions as fn
import sys
import data as dt
import datetime
import visualizations as vs

3. Data Description

---

To complete the project objective, we need to have the data that was created in the laboratory number two. Here are the three stories of operations that will be analize in the next steps.

data_ini_1 = dt.f_leer_archivo(param_estudiante=1)
data_ini_2 = dt.f_leer_archivo(param_estudiante=2)
data_ini_3 = dt.f_leer_archivo(param_estudiante=3)

data_ini_1

	Unnamed: 0	OpenTime	Symbol	Ticket	Type	Volume	OpenPrice	S/L	T/P	CloseTime	ClosePrice	Profit
0	0	2021-09-22 05:28:08	BTCUSD	45458917	0	0.23	42127.15	40139.0	42496.76	2021-09-22 05:28:17	42010.08	-26.93
1	2	2021-09-22 05:30:14	BTCUSD	45458934	0	0.23	42060.66	40143.0	42496.76	2021-09-22 10:43:00	42501.69	101.44
2	4	2021-09-22 17:27:04	BTCUSD	45470462	0	0.24	42599.23	0.0	0.00	2021-09-22 17:27:55	43025.30	106.91
3	7	2021-09-23 04:09:17	BTCUSD	45483777	1	0.23	43179.98	44046.0	42790.00	2021-09-23 06:24:19	44050.76	-200.28
4	9	2021-09-24 21:22:51	BTCUSD	45524436	0	0.23	42255.15	40304.0	43135.80	2021-09-25 00:39:11	43153.89	206.71

data_ini_2

	Unnamed: 0	OpenTime	Symbol	Ticket	Type	Volume	OpenPrice	S/L	T/P	CloseTime	ClosePrice	Profit
0	1	2021-09-20 21:38:54	GBPUSD	45426490	0	0.1	1.36482	1.34191	1.41691	2021-09-21 18:10:07	1.36541	5.90
1	2	2021-09-21 18:06:22	EURUSD	45448836	1	0.1	1.17224	1.17272	1.17072	2021-09-21 19:23:28	1.17271	-4.70
2	3	2021-09-22 17:22:53	AUDUSD	45470330	1	0.2	0.72540	0.73067	0.71567	2021-09-23 16:47:35	0.73067	-105.40
3	4	2021-09-23 16:58:56	USDCAD	45498648	0	0.1	1.26548	1.26062	1.27189	2021-09-24 05:42:33	1.26699	11.92
4	5	2021-09-24 19:23:01	NZDUSD	45522977	0	0.1	0.70234	0.70024	70507.00000	2021-09-24 23:56:35	0.70010	-22.40

data_ini_3

	Unnamed: 0	OpenTime	Symbol	Ticket	Type	Volume	OpenPrice	S/L	T/P	CloseTime	ClosePrice	Profit
0	0	2021-09-21 04:36:26	EURUSD	45431784	1	0.10	1.172670	1.17370	1.16980	2021-09-21 04:57:00	1.1737	-10.40
1	2	2021-09-22 06:36:42	EURUSD	45459384	0	0.25	1.172310	1.16211	1.18220	2021-09-22 21:36:26	0.0000	74.50
2	4	2021-09-23 06:01:16	EURUSD	45484785	0	0.50	1.170060	1.16186	1.77991	2021-09-23 15:57:25	0.0000	132.00
3	6	2021-09-23 16:03:02	USDCHF	45495654	0	0.70	1.370990	0.88129	0.95338	2021-09-24 23:01:56	0.0000	0.00
4	7	2021-09-23 16:08:36	GBPUSD	45496076	1	10.00	1.265510	1.42103	1.33297	2021-09-23 16:25:00	0.0000	957.05
5	9	2021-09-23 16:33:58	USDCAD	45497181	1	20.00	1.264810	1.28521	1.25619	2021-09-23 21:26:22	0.0000	1943.07
6	11	2021-09-23 21:31:00	USDCAD	45504414	0	0.70	0.923620	1.24462	1.27466	2021-09-24 05:11:08	0.0000	115.01
7	12	2021-09-23 21:41:07	USDJPY	45504470	1	25.00	110.216000	111.20600	109.72000	2021-09-28 06:25:44	111.2060	-22300.55
8	16	2021-09-29 20:21:17	USDCAD	45604366	1	75.00	1.276138	1.36050	1.25604	2021-09-30 05:59:52	0.0000	13765.90
9	20	2021-09-30 06:19:10	USDJPY	45610096	1	65.00	1.276138	114.82900	108.82400	2021-09-30 18:24:49	0.0000	17777.56

4. Descriptive statistics

---

data_jp = fn.f_columnas_tiempos(data_ini_3)
data_jp["mult"] = data_jp.Symbol.map(lambda x: fn.f_pip_size(x))
data_jp = fn.f_columnas_pips(data_jp)
parte_1_jp = fn.f_estadisticas_ba(data_jp)
parte_1_jp['df_1_tabla']

	medida	valor	descripcion
0	Ops totales	10.00000	Operaciones totales
1	Ganadoras	7.00000	Operaciones ganadoras
2	Ganadoras_c	0.00000	Operaciones ganadoras de compra
3	Ganadoras_v	0.00000	Operaciones ganadoras de venta
4	Perdedoras	2.00000	Operaciones eprdedoras
5	Perdedoras_c	0.00000	Operaciones perdedoras de compra
6	Perdedoras_v	0.00000	Operaciones eprdedoras de venta
7	Mediana (Profit)	1245.41400	Mediana de profit de operaciones
8	Mediana (Pips)	8445.26938	Mediana de pips de operaciones
9	r_efectividad	0.70000	Ganadoras Totales/Operaciones Totales
10	r_proporcion	3.50000	Ganadoras Totales/Perdedoras Totales
11	r_efectividad_c	0.00000	Ganadoras Compras/Operaciones Totales
12	r_efectividad_v	0.00000	Ganadoras Ventas / Operaciones Totales

parte_1_jp['df_2_ranking']

	Symbol	rank
0	EURUSD	66.67%
1	GBPUSD	100.00%
2	USDCAD	100.00%
3	USDCHF	0.00%
4	USDJPY	50.00%

data_dou = fn.f_columnas_tiempos(data_ini_1)
data_dou["mult"] = data_dou.Symbol.map(lambda x: fn.f_pip_size(x))
data_dou = fn.f_columnas_pips(data_dou)
parte_1_dou = fn.f_estadisticas_ba(data_dou)
parte_1_dou['df_1_tabla']

	medida	valor	descripcion
0	Ops totales	5.00	Operaciones totales
1	Ganadoras	3.00	Operaciones ganadoras
2	Ganadoras_c	0.00	Operaciones ganadoras de compra
3	Ganadoras_v	0.00	Operaciones ganadoras de venta
4	Perdedoras	2.00	Operaciones eprdedoras
5	Perdedoras_c	0.00	Operaciones perdedoras de compra
6	Perdedoras_v	0.00	Operaciones eprdedoras de venta
7	Mediana (Profit)	37.57	Mediana de profit de operaciones
8	Mediana (Pips)	-50391.00	Mediana de pips de operaciones
9	r_efectividad	0.60	Ganadoras Totales/Operaciones Totales
10	r_proporcion	1.50	Ganadoras Totales/Perdedoras Totales
11	r_efectividad_c	0.00	Ganadoras Compras/Operaciones Totales
12	r_efectividad_v	0.00	Ganadoras Ventas / Operaciones Totales

parte_1_dou['df_2_ranking']

	Symbol	rank
0	BTCUSD	60.00%

data_ap = fn.f_columnas_tiempos(data_ini_2)
data_ap["mult"] = data_ap.Symbol.map(lambda x: fn.f_pip_size(x))
data_ap = fn.f_columnas_pips(data_ap)
parte_1_ap = fn.f_estadisticas_ba(data_ap)
parte_1_ap['df_1_tabla']

	medida	valor	descripcion
0	Ops totales	5.000000	Operaciones totales
1	Ganadoras	2.000000	Operaciones ganadoras
2	Ganadoras_c	0.000000	Operaciones ganadoras de compra
3	Ganadoras_v	0.000000	Operaciones ganadoras de venta
4	Perdedoras	3.000000	Operaciones eprdedoras
5	Perdedoras_c	0.000000	Operaciones perdedoras de compra
6	Perdedoras_v	0.000000	Operaciones eprdedoras de venta
7	Mediana (Profit)	-22.936000	Mediana de profit de operaciones
8	Mediana (Pips)	-11.200000	Mediana de pips de operaciones
9	r_efectividad	0.400000	Ganadoras Totales/Operaciones Totales
10	r_proporcion	0.666667	Ganadoras Totales/Perdedoras Totales
11	r_efectividad_c	0.000000	Ganadoras Compras/Operaciones Totales
12	r_efectividad_v	0.000000	Ganadoras Ventas / Operaciones Totales

parte_1_ap['df_2_ranking']

	Symbol	rank
0	AUDUSD	0.00%
1	EURUSD	0.00%
2	GBPUSD	100.00%
3	NZDUSD	0.00%
4	USDCAD	100.00%

5. Performance Attribution Metrics

---

parte_2_1_dou = fn.f_evolucion_capital(cap_ini=100000, operaciones=data_dou.copy())
parte_2_2_dou = fn.f_estadisticas_mad(parte_2_1_dou, rf=0.05, benchmark_ticker="^GSPC")
parte_2_1_dou

[*********************100%***********************]  1 of 1 completed

	timestamp	profit_d	profit_acm_d
0	2021-09-22 23:59:59	181.42	100181.42
1	2021-09-23 23:59:59	-200.28	99981.14
2	2021-09-24 23:59:59	0.00	99981.14
3	2021-09-25 23:59:59	206.71	100187.85

parte_2_2_dou

	0	formato	valor	descripcion
0	sharpe_original	Cantidad	-1.54836	Sharpe Ratio Fórmula Original
1	sharpe_actualizado	Cantidad	-12.61735	Sharpe Ratio Fórmula Ajustada
2	drawdown_capi	Fecha Inicial	2021-09-22 23:59:59	Fecha inicial del DrawDown de Capital
3	drawdown_capi	Fecha Final	2021-09-23 23:59:59	Fecha final del DrawDown de Capital
4	drawdown_capi	DrawDown $ (capital)	-200.28	Máxima pérdida flotante registrada
5	drawup_capi	Fecha Inicial	2021-09-23 23:59:59	Fecha inicial del DrawUp de Capital
6	drawup_capi	Fecha Final	2021-09-25 23:59:59	Fecha final del DrawUp de Capital
7	drawup_capi	DrawUp $ (capital)	206.71	Máxima ganancia flotante registrada

parte_2_1_ap = fn.f_evolucion_capital(cap_ini=100000, operaciones=data_ap.copy())
parte_2_2_ap = fn.f_estadisticas_mad(parte_2_1_ap, rf=0.05, benchmark_ticker="^GSPC")
parte_2_1_ap

[*********************100%***********************]  1 of 1 completed

	timestamp	profit_d	profit_acm_d
0	2021-09-20 23:59:59	0.00	100000.00
1	2021-09-21 23:59:59	1.20	100001.20
2	2021-09-22 23:59:59	0.00	100001.20
3	2021-09-23 23:59:59	-105.40	99895.80
4	2021-09-24 23:59:59	-10.48	99885.32

parte_2_2_ap

	0	formato	valor	descripcion
0	sharpe_original	Cantidad	-6.157319	Sharpe Ratio Fórmula Original
1	sharpe_actualizado	Cantidad	-14.128447	Sharpe Ratio Fórmula Ajustada
2	drawdown_capi	Fecha Inicial	2021-09-21 23:59:59	Fecha inicial del DrawDown de Capital
3	drawdown_capi	Fecha Final	2021-09-24 23:59:59	Fecha final del DrawDown de Capital
4	drawdown_capi	DrawDown $ (capital)	-115.88	Máxima pérdida flotante registrada
5	drawup_capi	Fecha Inicial	2021-09-20 23:59:59	Fecha inicial del DrawUp de Capital
6	drawup_capi	Fecha Final	2021-09-21 23:59:59	Fecha final del DrawUp de Capital
7	drawup_capi	DrawUp $ (capital)	1.2	Máxima ganancia flotante registrada

parte_2_1_jp = fn.f_evolucion_capital(cap_ini=100000, operaciones=data_jp.copy())
parte_2_2_jp = fn.f_estadisticas_mad(parte_2_1_jp, rf=0.05, benchmark_ticker="^GSPC")
parte_2_1_jp

[*********************100%***********************]  1 of 1 completed

	timestamp	profit_d	profit_acm_d
0	2021-09-21 23:59:59	-10.40	99989.60
1	2021-09-22 23:59:59	74.50	100064.10
2	2021-09-23 23:59:59	3032.12	103096.22
3	2021-09-24 23:59:59	115.01	103211.23
4	2021-09-25 23:59:59	0.00	103211.23
5	2021-09-26 23:59:59	0.00	103211.23
6	2021-09-27 23:59:59	0.00	103211.23
7	2021-09-28 23:59:59	-22300.55	80910.68
8	2021-09-29 23:59:59	0.00	80910.68
9	2021-09-30 23:59:59	31543.46	112454.14

parte_2_2_jp

	0	formato	valor	descripcion
0	sharpe_original	Cantidad	-0.016109	Sharpe Ratio Fórmula Original
1	sharpe_actualizado	Cantidad	1.614683	Sharpe Ratio Fórmula Ajustada
2	drawdown_capi	Fecha Inicial	2021-09-24 23:59:59	Fecha inicial del DrawDown de Capital
3	drawdown_capi	Fecha Final	2021-09-28 23:59:59	Fecha final del DrawDown de Capital
4	drawdown_capi	DrawDown $ (capital)	-22300.55	Máxima pérdida flotante registrada
5	drawup_capi	Fecha Inicial	2021-09-28 23:59:59	Fecha inicial del DrawUp de Capital
6	drawup_capi	Fecha Final	2021-09-30 23:59:59	Fecha final del DrawUp de Capital
7	drawup_capi	DrawUp $ (capital)	31543.46	Máxima ganancia flotante registrada

6. Behavioral Finance

---

final_dict_dou = fn.f_be_de(data_dou)
final_dict_dou

 ++++ init_login succeded, message =  True
sin ocurrencias

final_dict_ap = fn.f_be_de(data_ap)
final_dict_ap

 ++++ init_login succeded, message =  True

{'ocurrencias': {'Cantidad': 1,
  'ocurrencia_1': {'timestamp': Timestamp('2021-09-21 18:10:07'),
   'operaciones': {'ganadoras': {'instrumento': 'GBPUSD',
     'volumen': 0.1,
     'sentido': 'buy',
     'profit_ganadora': 5.9},
    'perdedoras': {'instrumento': 'EURUSD',
     'volumen': 0.1,
     'sentido': 'buy',
     'profit_perdedora': -0.0}},
   'ratio_cp_profit_acm': 0.0,
   'ratio_cg_profit_acm': 1.0,
   'ratio_cp_cg': 0.0}},
 'resultados': {'dataframe':    ocurrencias status_quo aversion_perdida sensibilidad_decreciente
  0            1     100.0%             0.0%                       No}}

final_dict_jp = fn.f_be_de(data_jp)
final_dict_jp

 ++++ init_login succeded, message =  True

{'ocurrencias': {'Cantidad': 4,
  'ocurrencia_1': {'timestamp': Timestamp('2021-09-23 16:25:00'),
   'operaciones': {'ganadoras': {'instrumento': 'GBPUSD',
     'volumen': 10.0,
     'sentido': 'sell',
     'profit_ganadora': 957.05},
    'perdedoras': {'instrumento': 'USDCHF',
     'volumen': 0.7,
     'sentido': 'sell',
     'profit_perdedora': 0.31}},
   'ratio_cp_profit_acm': 0.0,
   'ratio_cg_profit_acm': 0.83,
   'ratio_cp_cg': 0.0},
  'ocurrencia_2': {'timestamp': Timestamp('2021-09-23 21:26:22'),
   'operaciones': {'ganadoras': {'instrumento': 'USDCAD',
     'volumen': 20.0,
     'sentido': 'sell',
     'profit_ganadora': 1943.07},
    'perdedoras': {'instrumento': 'USDCHF',
     'volumen': 0.7,
     'sentido': 'sell',
     'profit_perdedora': 0.31}},
   'ratio_cp_profit_acm': 0.0,
   'ratio_cg_profit_acm': 0.63,
   'ratio_cp_cg': 0.0},
  'ocurrencia_3': {'timestamp': Timestamp('2021-09-24 05:11:08'),
   'operaciones': {'ganadoras': {'instrumento': 'USDCAD',
     'volumen': 0.7,
     'sentido': 'buy',
     'profit_ganadora': 115.01},
    'perdedoras': {'instrumento': 'USDCHF',
     'volumen': 0.7,
     'sentido': 'buy',
     'profit_perdedora': 0.31}},
   'ratio_cp_profit_acm': 0.0,
   'ratio_cg_profit_acm': 0.04,
   'ratio_cp_cg': 0.0},
  'ocurrencia_4': {'timestamp': Timestamp('2021-09-24 05:11:08'),
   'operaciones': {'ganadoras': {'instrumento': 'USDCAD',
     'volumen': 0.7,
     'sentido': 'buy',
     'profit_ganadora': 115.01},
    'perdedoras': {'instrumento': 'USDJPY',
     'volumen': 25.0,
     'sentido': 'buy',
     'profit_perdedora': -6.63}},
   'ratio_cp_profit_acm': 0.0,
   'ratio_cg_profit_acm': 0.04,
   'ratio_cp_cg': 0.06}},
 'resultados': {'dataframe':    ocurrencias status_quo aversion_perdida sensibilidad_decreciente
  0            4     100.0%             0.0%                       No}}

7. Visualizations and Results

---

7.1 Results of Descriptive statistics and Performance Attribution Metrics

# Ranking
parte_1_ap['df_2_ranking']['rank'] = parte_1_ap['df_2_ranking']['rank'].map(lambda x: eval(x[:-1]))
vs.pie_chart(parte_1_ap['df_2_ranking'], title="Ranking Ana Paula")

parte_1_dou['df_2_ranking']['rank'] = parte_1_dou['df_2_ranking']['rank'].map(lambda x: eval(x[:-1]))
vs.pie_chart(parte_1_dou['df_2_ranking'], title="Ranking Douglas")

parte_1_jp['df_2_ranking']['rank'] = parte_1_jp['df_2_ranking']['rank'].map(lambda x: eval(x[:-1]))
vs.pie_chart(parte_1_jp['df_2_ranking'], title="Ranking Juan Pablo")

# DrawDown and DrawUp
vs.line_chart(parte_2_1_ap, parte_2_2_ap)

vs.line_chart(parte_2_1_jp, parte_2_2_jp)

vs.line_chart(parte_2_1_dou, parte_2_2_dou)

7.2 Results of Behavioral Finance

# Disposition Effect
vs.chart_behave(final_dict_dou)

sin ocurrencias

vs.chart_behave(final_dict_ap)

vs.chart_behave(final_dict_jp)

8. Conclusions

9. References

---

[1] Munnoz, 2020. Python project template. https://github.com/iffranciscome/python-project. (2021).